Background: The term “corporate culture” is used to describe a company’s long-standing norms and practices, as well as the staff’s views and the anticipated value of their job. Executives may need to adjust their leadership styles to achieve the organization’s goal, which may have consequences for the satisfaction of the workforce. Therefore, it is essential to appreciate the relationship between business ethos, management style, work performance, mental health and employees’ job satisfaction. Methods: Researchers was conducting a cross-sectional survey of Saudi Arabian and Indian employees. Data was be collected using a structured questionnaire. To test the reliability of the data, they will be analysed by “Cronbach’s a and confirmatory factors”. SEM was be used to show the relationships of organizational cultures and leadership behaviour on work performance, mental health and job satisfaction through IBM-SPSS and SmartPLS software. Scope: A corporation with a strong culture and effective leadership shares principles and norms of behaviour with its workers, which should aid them in attaining their goals and objectives. Employees could gain work recognition, mental piece, work performance and job satisfaction when they can accomplish the obligations allotted to them by the company. Results: Corporate culture were significantly (positively) correlated with work performance, mental health and job satisfaction. In the same way, leadership behavior was significantly (positively) correlated with work performance, mental health and job satisfaction. Conclusions: The organisational culture holds significant importance, exerting a substantial influence on the overall well-being and productivity of the work environment. The acknowledgement and acceptance of the organisational ethos by workers can have a significant impact on their work behaviour and attitudes when it comes to communication and promotion. When there is a positive interaction between leadership and employees, the latter are more likely to actively contribute to team collaboration and interaction. Additionally, they are more likely to be motivated to achieve the organization’s assigned mission and objectives. As a result, work performance, mental health, and job satisfaction are enhanced.

In the dynamic contemporary business landscape, the convergence of technology, finance, and management plays a pivotal role in organizational success. This research explores the multifaceted realm of strategic integration, emphasizing the intricate balance between these domains. The background sets the stage, elucidating the historical evolution and growing relevance of this integration. Various research methodologies, including case studies, surveys, interviews, and data analysis, are used to investigate practical aspects. The study delves into the role of technology, emphasizing digital transformation, innovation, and IT infrastructure. It dissects financial management, focusing on decision-making, risk management, and capital allocation. Additionally, management and leadership are discussed, with an emphasis on change management, strategic leadership, and skill development. Challenges, such as cultural disparities and regulatory complexities, are scrutinized, alongside opportunities like improved decision-making and enhanced productivity. Real-world case studies illustrate success stories and lessons learned. The paper concludes with findings, implications for businesses and management, and practical recommendations for navigating this convergence. This research contributes valuable insights into performance and competitiveness, facilitating a better understanding of key performance metrics and positioning strategies in the digital age.

Recently, the government of Ethiopia has been engaged in modernizing the trans-regional Ethio-Djibouti railway infrastructure using the Belt and Road Initiative. This railway corridor has been serving as the main get way for the landlocked Ethiopia to the port. This article creates an insight about the implications of the Ethio-Djibouti railway corridor by exploring the question: what kinds of urban form and morphological changes evolved due to the railway corridor? To examine the impact of this railway corridor, the article employed stratified sampling and multiple criteria intermediate cities selection method. Accordingly, four (Bishoftu, Mojo, Adama, and Dire Dawa) intermediate cities were selected as case study. The article points out that the railway corridor conceived different kinds of linear urban centers around stations. The identified four intermediate cities attract industries and logistic centers. Those industries, logistic centers, and new railway stations often established at the periphery of intermediate cities resulted labour influx from rural and nearby small urban centers and urban expansion that caused a rural-urban continuum of ribbon settlement and strengthen trade gate way for the landlocked Ethiopia that caused trans-regional integration.

With the rapid development of modernization and the reform and development of quality education, the main direction and goal of vocational colleges in the new era is to cultivate high-level skilled talents required by the times. With the development of globalization and the refined division of labor in industrial technology, the requirements of various industries for high-level skilled talents with the ability to adapt to market development are gradually increasing. This article focuses on exploring and analyzing the demand for hospital imaging technology talents under the rapid development of the new era industry, and discovering the problems in talent cultivation in vocational colleges. In response to the existing problems, actively utilizing college resources and practical opportunities, innovating the college school cooperation mode and teaching methods for imaging technology majors in vocational colleges, and gradually expanding into a standardized, scientific, and developable college cooperation mode for vocational education, Implement the national strategic plan for cultivating quality talents in vocational colleges, focus on doing a good job in the work of "cultivating morality and talents", adhere to the "three education" reform, and improve the quality of talent cultivation.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.